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1 – 10 of 10Jani Saastamoinen, Arsen Djatej, Kati Pajunen and M. David Gorton
Accounting standards for goodwill may intensify the agency conflict. Since auditors evaluate intangible asset valuations, this study examines to what extent being an auditor…
Abstract
Purpose
Accounting standards for goodwill may intensify the agency conflict. Since auditors evaluate intangible asset valuations, this study examines to what extent being an auditor (including Big 4 auditors) and being female as indicators of professional skepticism and conservatism predict accounting professionals' critical views of goodwill accounting under US GAAP.
Design/methodology/approach
Statistical analyses of a survey of accounting professionals in the Pacific Northwest region of the United States.
Findings
The respondents' views are dispersed from trust in GAAP to views reflecting management opportunism in goodwill accounting. While being an auditor (including Big 4 auditors) does not predict a critical perception, being a female auditor is correlated with critical views to some extent.
Research limitations/implications
The survey was carried out in a limited geographical area and personal contacts were used to maximize the response rate, which may limit generalizability.
Practical implications
Standard setters can use the results to learn how practitioners perceive the current accounting standards for goodwill. The results provide users and preparers knowledge about potential pitfalls of goodwill accounting. Preparers could increase transparency to alleviate user concerns regarding managerial opportunism in goodwill accounting.
Originality/value
This paper extends the IFRS-based literature exploring practitioners' perceptions of accounting standards by focusing on goodwill accounting in the US GAAP environment. This study also contributes to the auditing literature by providing further evidence on how gender moderates an auditor's perception of accounting standards.
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Linda Ponta, Gloria Puliga and Raffaella Manzini
The measure of companies' Innovation Performance is fundamental for enhancing the value and decision-making processes of firms. The purpose of this paper is to present a new…
Abstract
Purpose
The measure of companies' Innovation Performance is fundamental for enhancing the value and decision-making processes of firms. The purpose of this paper is to present a new measure of Innovation Performance, called Innovation Patent Index (IPI), which makes it possible to quantitatively summarize different aspects of firms' innovation.
Design/methodology/approach
In order to define the IPI, a secondary source, i.e. patent data, has been used. The five dimensions of IPI, i.e. efficiency, time, diversification, quality and internationalization have been defined both analyzing the literature and applying three different machine learning algorithms (regularized least squares, deep neural networks and decision trees), considering patent forward citations as a proxy of the innovation performance.
Findings
Results show that the IPI index is a very useful tool, simple to use and very promptly. In fact, it is possible to get important results without making time consuming analysis with primary sources. It is a tool that can be used by managers, businessmen, policymakers, organizations, patent experts and financiers to evaluate and plan future activities, to enhance the innovation capability, to find financing and to support and improve innovation.
Research limitations/implications
Patent data are not widely used in all the sectors. Moreover, the pure number of forward citations is not the only forward looking indicator suggested by the literature.
Originality/value
The demand for a useable Innovation Performance tool, as well as the lack of tools able to grasp different aspects of the innovation, highlight the need to develop new instruments. In fact, although previous studies provide several measures of Innovation Performance, these are often difficult for managers to use, do not appreciate different aspects of the innovation and are not forward looking.
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Vinicius Muraro and Sergio Salles-Filho
Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes…
Abstract
Purpose
Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.
Design/methodology/approach
The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.
Findings
It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.
Originality/value
This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.
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